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# FastAI-Compatible Colorization Models Guide
## Current Issue
The model `Hammad712/GAN-Colorization-Model` contains a PyTorch model (`generator.pt`), not a FastAI model. FastAI models must be `.pkl` files created with FastAI's `export()` function.
## How to Find FastAI-Compatible Models
### Option 1: Search Hugging Face
1. Go to https://huggingface.co/models
2. Search for: `fastai colorization` or `fastai image colorization`
3. Look for models that have `.pkl` files in their repository
4. Check the model's README to confirm it's a FastAI Learner
### Option 2: Use FastAI's Official Examples
FastAI course examples often have colorization models. Look for:
- FastAI course lesson notebooks on image colorization
- Models exported using `learn.export('model.pkl')`
### Option 3: Train Your Own
If you have a FastAI colorization model:
```python
from fastai.vision.all import *
learn = ... # your trained model
learn.export('model.pkl')
```
Then upload `model.pkl` to Hugging Face.
## Setting a New Model
### Via Environment Variable (Recommended)
In your Hugging Face Space settings, add:
```
MODEL_ID=your-username/your-fastai-colorization-model
```
### Via Code
Update `app/config.py`:
```python
MODEL_ID: str = os.getenv("MODEL_ID", "your-username/your-fastai-colorization-model")
```
## Model Requirements
The model must:
1. ✅ Be a FastAI Learner exported as `.pkl` file
2. ✅ Accept PIL Images as input
3. ✅ Return colorized images (PIL Image or tensor)
4. ✅ Be uploaded to Hugging Face Hub
## Testing a Model
Before switching, you can test locally:
```python
from huggingface_hub import from_pretrained_fastai
from PIL import Image
learn = from_pretrained_fastai("your-model-id")
img = Image.open("test.jpg")
result = learn.predict(img)
```
If this works, the model is compatible!
## Alternative: Switch Back to SDXL+ControlNet
If you can't find a FastAI model, you can switch back to the SDXL+ControlNet approach which was working before. Update `MODEL_BACKEND` to `"diffusers"` and use a ControlNet colorization model.
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